Siemens Logistics has introduced SmartService, a portfolio of solutions designed to minimize downtime in parcel sorting centers and airport baggage handling systems thanks to predictive maintenance.
Collected data helps detect changes in the condition of systems and their components at an early stage. Necessary measures such as service, repair and simple cleaning are carried out at the optimum time and resources are used in the best possible way.
Siemens’ service approach is based on condition monitoring of the systems, in which mobile and stationary sensors record, for example, vibration and distance measurements of rails and belts as well as forces on chains. If deviations from threshold values established from historical data analysis are identified, users can plan and carry out targeted maintenance measures and thus avoid downtimes.
The predictive maintenance approach makes use of advances made in digitalization. Smart applications and machine learning algorithms evaluate collected data and predict the remaining life of components, such as sorter carriers, belts and motors. To store and analyze the data obtained, Siemens offers the open, cloud-based IoT operating system MindSphere. Evaluations and recommended actions are displayed on user-friendly dashboards.